{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "Viewing the Pima Indians Diabetes Dataset with Pandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>pregnancy_x</th>\n",
       "      <th>plasma_con</th>\n",
       "      <th>blood_pressure</th>\n",
       "      <th>skin_mm</th>\n",
       "      <th>insulin</th>\n",
       "      <th>bmi</th>\n",
       "      <th>pedigree_func</th>\n",
       "      <th>age</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6</td>\n",
       "      <td>148</td>\n",
       "      <td>72</td>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "      <td>33.6</td>\n",
       "      <td>0.627</td>\n",
       "      <td>50</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>85</td>\n",
       "      <td>66</td>\n",
       "      <td>29</td>\n",
       "      <td>0</td>\n",
       "      <td>26.6</td>\n",
       "      <td>0.351</td>\n",
       "      <td>31</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8</td>\n",
       "      <td>183</td>\n",
       "      <td>64</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>23.3</td>\n",
       "      <td>0.672</td>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>89</td>\n",
       "      <td>66</td>\n",
       "      <td>23</td>\n",
       "      <td>94</td>\n",
       "      <td>28.1</td>\n",
       "      <td>0.167</td>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>137</td>\n",
       "      <td>40</td>\n",
       "      <td>35</td>\n",
       "      <td>168</td>\n",
       "      <td>43.1</td>\n",
       "      <td>2.288</td>\n",
       "      <td>33</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   pregnancy_x  plasma_con  blood_pressure  skin_mm  insulin   bmi  \\\n",
       "0            6         148              72       35        0  33.6   \n",
       "1            1          85              66       29        0  26.6   \n",
       "2            8         183              64        0        0  23.3   \n",
       "3            1          89              66       23       94  28.1   \n",
       "4            0         137              40       35      168  43.1   \n",
       "\n",
       "   pedigree_func  age  target  \n",
       "0          0.627   50       1  \n",
       "1          0.351   31       0  \n",
       "2          0.672   32       1  \n",
       "3          0.167   21       0  \n",
       "4          2.288   33       1  "
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "data_web_address = \"https://archive.ics.uci.edu/ml/machine-learning-databases/pima-indians-diabetes/pima-indians-diabetes.data\"\n",
    "column_names = ['pregnancy_x', \n",
    "                'plasma_con', \n",
    "                'blood_pressure', \n",
    "                'skin_mm', \n",
    "                'insulin', \n",
    "                'bmi', \n",
    "                'pedigree_func', \n",
    "                'age', \n",
    "                'target']\n",
    "\n",
    "feature_names = column_names[:-1]\n",
    "all_data = pd.read_csv(data_web_address , names=column_names)\n",
    "all_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>pregnancy_x</th>\n",
       "      <th>plasma_con</th>\n",
       "      <th>blood_pressure</th>\n",
       "      <th>skin_mm</th>\n",
       "      <th>insulin</th>\n",
       "      <th>bmi</th>\n",
       "      <th>pedigree_func</th>\n",
       "      <th>age</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3.845052</td>\n",
       "      <td>120.894531</td>\n",
       "      <td>69.105469</td>\n",
       "      <td>20.536458</td>\n",
       "      <td>79.799479</td>\n",
       "      <td>31.992578</td>\n",
       "      <td>0.471876</td>\n",
       "      <td>33.240885</td>\n",
       "      <td>0.348958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3.369578</td>\n",
       "      <td>31.972618</td>\n",
       "      <td>19.355807</td>\n",
       "      <td>15.952218</td>\n",
       "      <td>115.244002</td>\n",
       "      <td>7.884160</td>\n",
       "      <td>0.331329</td>\n",
       "      <td>11.760232</td>\n",
       "      <td>0.476951</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.078000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>62.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>27.300000</td>\n",
       "      <td>0.243750</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>117.000000</td>\n",
       "      <td>72.000000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>30.500000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>0.372500</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>6.000000</td>\n",
       "      <td>140.250000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>127.250000</td>\n",
       "      <td>36.600000</td>\n",
       "      <td>0.626250</td>\n",
       "      <td>41.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>17.000000</td>\n",
       "      <td>199.000000</td>\n",
       "      <td>122.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>846.000000</td>\n",
       "      <td>67.100000</td>\n",
       "      <td>2.420000</td>\n",
       "      <td>81.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       pregnancy_x  plasma_con  blood_pressure     skin_mm     insulin  \\\n",
       "count   768.000000  768.000000      768.000000  768.000000  768.000000   \n",
       "mean      3.845052  120.894531       69.105469   20.536458   79.799479   \n",
       "std       3.369578   31.972618       19.355807   15.952218  115.244002   \n",
       "min       0.000000    0.000000        0.000000    0.000000    0.000000   \n",
       "25%       1.000000   99.000000       62.000000    0.000000    0.000000   \n",
       "50%       3.000000  117.000000       72.000000   23.000000   30.500000   \n",
       "75%       6.000000  140.250000       80.000000   32.000000  127.250000   \n",
       "max      17.000000  199.000000      122.000000   99.000000  846.000000   \n",
       "\n",
       "              bmi  pedigree_func         age      target  \n",
       "count  768.000000     768.000000  768.000000  768.000000  \n",
       "mean    31.992578       0.471876   33.240885    0.348958  \n",
       "std      7.884160       0.331329   11.760232    0.476951  \n",
       "min      0.000000       0.078000   21.000000    0.000000  \n",
       "25%     27.300000       0.243750   24.000000    0.000000  \n",
       "50%     32.000000       0.372500   29.000000    0.000000  \n",
       "75%     36.600000       0.626250   41.000000    1.000000  \n",
       "max     67.100000       2.420000   81.000000    1.000000  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xa322668>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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8/DwAnU6HUYzc0Uv6a+B3gaeAU4HnAZ8FfhnoRsSipDng3og4v+bx7ui91thrmW02G9rR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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xa316f60>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "#The default is bins=10 which is hard to read in the visualization.\n",
    "all_data.pregnancy_x.hist(bins=50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x000000000A493B00>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x000000000A7215C0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x000000000A7EDE48>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x000000000A8E03C8>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x000000000A998C50>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x000000000AA467B8>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x000000000AB56358>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x000000000AC50BE0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x000000000ACFF550>]], dtype=object)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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EvlYx+0jgnPz+OcB7Ox2XmVk9kuZKul7Sakm3SVqWz/c+lzUoqxUu3w466CAG\nB4eKDspapKkaOklfB34UEWdLmgPsAJwMPBERn5N0ArAgIk6s8dy21tDVqqFw22GzxvVqLYqkS4DP\nADsBfx0RR0haHxELKpZZFxEL6zzftSiJcw1d/+rVvAUgafuIeFrS1sCPgeOAP2SG+1w2c71SQ+e8\nk6ZW5K1Z93IpaUfgLRGxFCAingOelHQkcGC+2Dlk15YnJRczs06T9C5gLCJKkoanWHTKX7ilS5cy\nNDQEwPz581myZAnDw9nLlZtveLrY6XHtmN5mQq+YCxYMsG7d2pbEl8r71y3TK1asoFQqbfk+9rKI\neDq/O5ds/y3IWhd4n6vFqjvWM0teRMzqBvwucD1wNnAzcAawPbC+arl1dZ4fZQMDLw+4JyACIubN\n2z3uu+++aAaw5fWyGzWXW7lyZVPraQXH4BiqpRBH/p2ZdY5I8QZ8FlgD3A88CmwEzgXuBAbyZQaB\nO6d4jda8wU1IYftIOYZa+b990yvr/r7UM/n16v9GNftedFIKMfRi3irfyMpkVgNPAafl82a8z1WU\nFLaPRmOYPgfMJm+U561s8nVbtczMc1erddM20U6tyFvNjEM3B9gP+ERE3CjpC2RnharPbNc9010+\ny71x4xPA14FDgWEAVq1axZo1a2Z91i4zsuX1ysvUO0ta9FnGoqdLpVLh8ZRKpcLfj7J+/DxKpRIb\nNmwAYHR0lF4UESeTNQtH0oFkTS4/LOlzwFJgOXAM8N3CgjQzqyEiNgP75i2kLpO0D7PY54JiWhZ0\n0298vhSV+5ATpye+Xuumq1+/3nR1PNXTjayvND6V+OfRa9PtaFkw6xo6SQPATyNij3z6zWQHdHsC\nwxExJmkQWBkRe9d4fpTX7Ro6s/T0ci0KTDigO0LSQuBi4GXAA8BREbGhzvPCuSRtnaiha+b3xTV0\n7dPreatM0qeBp4GPMsN9Lpve9Dmk1rz06uMaWcbbRfEKHYcuIsaAByW9Mp91MPBz4HKyM93gM91m\nlqiI+FFEHJHfXxcRh0TEXhFxWL2DOTOzIkh6cbkHS0kvIGvSdCfe5zIzmh+H7jjgPEklspq6z5I1\nWTpU0t1kB3mnN7mOtqq+7OsYHEMKUonD0pTC9lFkDIODQxO63y626+2RAtc9rt+3iT7wUmBlvr91\nPXBlRFxBF+1zpbB9pBBDKjkjhThS+DxSiKEVmqmhIyJuAd5Q46FDmnldMzOzerLe54JyzcjYWM+3\nsLM+FxHpQVx5AAAgAElEQVS3kfVbUD1/Hd7nMut7TY1D19SKXUNnlrR+qUWZKUnx7nd/EIAXv3gB\n//qvn2e77bYrOKr+Ml1+dw1d/3Leqs01dDPjGjrrpEJr6MzM+tX3v/9uvv/9d3PBBRfz6KOPFh2O\nMXdCE0wzM7N+0vcHdCm0nXUMjqFaKnFYPUcDR7PNNvMKWXsK20cKMYzXgGwiO/NcvhURQ7FS+DxS\niMHSlcL2kUIMqeSMFOJI4fNIIYZW6PsDOjMzMzMzs27lGjozq6lXa1EkzQX+A9iWrGOob0XEqZIW\nABcBi4FRsrHonqzx/CjnlnnzdufWW69l991371T4RjE1cq6h6w69mrea5Rq6mXENnXWSa+jMzGYo\nIjYBB0XEvsAS4HBJ+wMnAtdExF7AtcBJBYZpZmZm1pC+P6BLoe2sY3AM1VKJo1dFxNP53blkV+kC\nOBI4J59/DvDeAkJrSArbRwoxpFADkkYMaXweKcRg6Uph+0ghhlRyRgpxpPB5pBBDK3TtAV1aA8ua\nWTeRtJWk1cBa4OqI+BkwEBFjABGxFti5yBjNzMzMGtG1NXSzGYfI7YTNGtcPtSiSdgQuA44D/jMi\nFlY89kREvKjGc1xDVzDX0Fk9/ZC3ZsM1dDPjGjrrpFbkrTmtCqbdBgeHGBt7oOgwzKyHRMRTkkaA\ndwBjkgYiYkzSIPBY/WcuBYbYtGk9Z511FgcffDDDw8PAePMNT7d3elwK09tMGP9uwYIB1q1bmz06\nw/hTeX+7ZXrFihWUSiWGhoYwM+tbEVHILVt1ZmDg5QH3BERAxLx5u8d9990XlYAtj2e3ydPTLV/L\nypUra87vJMfgGKqlEEf+nSksR7TrBrwY2Cm//wKyHi/fCSwHTsjnnwCcXuf5FblqKO6///6WvN8z\nkcL2UWQM45/Byrq/B52bXlnj8bn5vOw2MLC4TvzT/0Y1qt+3ibJezVvN3prdvlohhe2j0RimzwGz\nyRvleSubfN1WLbOy6bzTrG7aJtqpFXmr6Rq6vBblZkmX59MLJF0l6W5JV0raqdl1mJm10EuBlZJK\nwPXAlRFxBdkB3aGS7gYOBk4vMEbrahMHOnfrEmuWpEWSrpX0c0m3SToun+99riZV98lQebXdrFs0\nXUMn6ZPA64AdI+IIScuBJyLic5JOABZExIk1nhfldTdSQ9dIzUTl/+IaOrPmuBalNtfQFS/FGrrm\nfp8mL2Oz06t5K28GPhgRJUnzgJvIeuY9lhnuc9lE9b6PU3+nG1mm+Pq4RpbxdlG8wsehk7SIrKnS\n1ypmd03X32ZmZmapi4i1EVHK728E7gQW4X0uM6P5YQu+AHyKiYf8XdX1dwrjTzgGx1AtlTgsTSls\nHynEkMI4SmnEkMbnkUIM/UDSELAEWEUX7XOlsH2kEEMqOSOFOFL4PFKIoRVm3culpHcBY/nl/+Ep\nFq17LXfp0qUMDQ2xceMTwNeBQ4HspVatWsWaNWu29GSVGdnyeK3pkZGRKZevfLz6Ayy6p66ip0ul\nUuHxlEqlwt+Psn78PEqlEhs2bABgdHQUMzNLS97c8lvA8RGxMWsCPsG0+1wA8+fPZ8mSJf6N37LP\nWH58uHKpKaYnvl7rpuvFUz1dHU/1dCPrK41PJfZ59Pp0O3rnnXUNnaTPAn8MPEfWU9wLycZzej0w\nHONdf6+MiL1rPN81dGYJ69ValGa5hq79qoepGRhYzNq1o1umXUNn9fRy3pI0B/g+8IOI+GI+705m\nuM9lE7mGzttF0QqtoYuIkyNit4jYA/gAcG1EfBj4HtkgTQDHAN+dzeu/8Y0HuschM7M+lB3MuZdI\nsypnAXeUD+Zyl9OCfS4zmNzj5+DgUNEhWYOaHraghtNpQdffjz/+EJU/6O1Sfdm3CI7BMVRLJQ5L\nUwrbRwoxpFADkkYMaXweKcTQqyQdAHwIeJuk1flwUe+gi4ZbSWH7SCGGVHJGrTg6fTIthc8jhRha\nYdY1dJUi4kfAj/L764BDWvG6ZmZmZv0uIn4MbF3nYe9z2SzNnaYV3OTHq5vAWxqaHodu1iuepoZu\n48Zf0toaBbcTNpuJXq1FyYdb+QYwAGwGvhoR/yxpAXARsBgYBY6KiCdrPN81dG02Xf52DZ3V06t5\nq1muoauv32voZvM/eFtqrcLHoTMz60LPAX8VEfsAvwd8QtKrgBOBayJiL+Ba4KQCYzQzMzNrSA8d\n0M2dVScqKbSddQyOoVoqcfSiXhigN4XtI4UY0qhFGSk6ACCNzyOFGCxdKWwfKcSQSs5IIY4UPo8U\nYmiFltTQpWETky8Tm5nVN9UAvZKSHaC3/0xX55G6bo/fzMxS1kMHdLMzPqjk1CrHRWp1QWijMbST\nY0gnBkgnjl7WzAC9WS/hQ2zatJ6zzjqLgw8+2AOytun/y84iV56wGwEOyu8P074BfhudrlZr+Xrx\nT14+lfd/NtPDw8MdX387Bui19kjhdy2FGCYOBl6k4aIDSOLzSCGGVuipTlFmUpQ+i3grXs8Fodb7\nerlzgWYH6HWnKO3VjZ2eNDedzfPvSvN6OW81w52i1OdOUZyriuZOUWZkYo1debDEFNrOOgbHUC2V\nOHpYVw/Qm8L2kUIMKdSApBFDGp9HCjFYulLYPlKIIZWckUIcKXweKcTQCn10QFdu8tKZwRI7aXBw\nqObBqplN1gsD9JqZmZmV9VWTy2aaYKbc5NJj7lk7uOlSbW5y2X5ucmmz5bxVm5tc1ucml85VRXOT\nSzMzMzMzsz7W9wd0RbSdrW4iuXDhYMdjqJZCG2LHMC6VOCxNKWwfKcSQQg1IGjGk8XmkEIOlK4Xt\nI4UYUskZKcSRwueRQgytMOsDOkmLJF0r6eeSbpN0XD5/gaSrJN0t6UpJO7Uu3PbpZB1aVr83Xs+3\nfv1Y29ZlZmZm3U3SmZLGJN1aMa8r97es29XuZNCKNesaurxb78GIKOXjOd0EHAkcCzwREZ+TdAKw\nICJOrPH8pGropqtDa2UNXatr3lxDZ+3gWpTaXEPXfq6hs9nq1bwl6c3ARuAbEfHafN5yGtjfypeN\nSy+9dMv0a17zGl7xild0IPL0uYau+f/Buas5rchbsx5YPCLWAmvz+xvzMZwWkR3UHZgvdg7ZNd2a\nCcbK5uYJJdPqgcvNzMyse0XEdZIWV82e0f7Wscd+E4Dnn1/HHns8x623XteGSNMyODg0qVfzrbba\nns2bny4ool40cR8WvB9bhJbU0EkaApYAq4CBiBiDLQd9O7diHa038ZJxsYofUiGFNsSOYVwqcVia\nUtg+UoghhRqQNGJI4/NIIYY+s/NM9reeeurbPPXUt/nNb07nmWee60iAlYrYPqpLXGBlfjBXOa/T\nRgpYZy0jLXqdifuwM9mPTSFnpBBDK8z6Cl1Z3tzyW8Dx+ZW66m9H3W/L0qVLGRoaYuPGJ4CvA4cC\nwxVLjLRxehOwMr8/THYJeeLyIyMjDA/Xfn55Ayg/PtPp8S9S7elmX2+mzy+VSk39P62YLpVKha6/\nUlHrL/LzKJVKbNiwAYDR0VF6laQzgXcDYxVNlxYAFwGLgVHgqIh4srAge1z1WXOfzTVriWmOTpYC\nQ8BDrFv36IR9nF79jR9Xb3q4znR5Xr3pRl9/ptP14qmero6nerqR9ZXaGl8j21flsrUe79XpFStW\nUCqVGBoaolWaGodO0hzg+8APIuKL+bw7geGIGMvr7FZGxN41nlt4Dd3sa+ymbi883c5KI/Uhsx8j\nb+bPN6vFtSj1a1FcQ9ecmdUsZ4/39nQ2z3m7eb2atwDyJpffq8hbDe1v5cvG+Da3ir32+p/cddeq\nzgReoPbVxzWyTOr1cY0sM/vXdT5rXArj0J0F3FE+mMtdTnYaCOAY4LtNrqPrVF/in3kTSvcgZNYu\nEXEdsL5q9pFkNSjkf9/b0aDMzKan/FbW9/tbZpZpZtiCA4APAW+TtFrSzZLeASwHDpV0N3AwcHpr\nQm2XkaIDqKHzNXWTmyd0nmMYl0ocfWRGtShFS2H7aG0Ms61pbmUMszVSdABAL24TVknS+cBPgFdK\nWiPpWLL9q67Z30pj+xgpOgDSiAFSiCOFbSKFGFqhmV4ufwxsXefhQ2b7uumY3GtPPbV6UZrta6XA\n9S1mhVTK97HySayy7smXZp0QEUfXeagH9rfMrFlNd4rS/YbrzG98B2O8iWW9ZdPfWSkXasLk/2ds\nrDPxVsZQlBRigHTi6CNjkgYqalEem3rxpcAQmzat56yzzuLggw/u2w582tdZQSPTw00+vxXT1Zp7\nvVQ+n9lMDw8P90TnAtYeafyuDRcdAGnEACnEkcI2kUIMrdBUpyhNrbgLOkWpP935Av6pPicPVG7t\n0OOdCwyRdS7wmnx6ObAuIpa7U5T2679OT9yJQKf0ct5qhjtFmTCXRr6P7hSludd1PmtcCp2i9ICR\nogNoQPs7SUmhDbFjGJdKHL3ItSi9E0Ma+Xuk6ACAND6PFGKwdKWxfYwUHQBpxAApxJHCNpFCDK3g\nJpddYWKTzU41gWwV1+RZSlyLYmmaWGvtPGlmZo1yk8tZTRc/ZtJ060+pyaWbcHYnN12qzU0um+cm\nl400Y9qO7GRexgd4jXHeqs1NLifMZebfRze5nOky3s9rXCvylq/QzUp39VppZmbdprtbZpiZWee4\nhm5WbYgnjhPXC1JoQ+wYxqUSh6Uphe1jqhgGB4faXvebR9Gm152JkULWWv0eL1w4WEgclVLYLi1d\naWwfI0UHQBoxQApxpLBNpBBDK/gKXVdq7RXC6cfRMzNr3OShT7Zzq4YWq36P16/3+2vm/RnrV66h\nm9V0SrHUnp7J59pIPYtr6PqPa1Fqcw3d9Fwj1466lPbWTvcK563a+qWGbvrcU2teevVn6SzjGrpO\n8LAFVkd7hznoXHMqMzPLTMzrZmbpmpivtt56hwnT3ndsPR/QJdCGuPUm1viNja2d8ouVGZni9SZ+\nMceb+pRfv7nmDTM9QGznAWUqbalTicPSlML2kUIMaeTvkQ6tp7na7enyZivyahrbhKUqje1jpOgA\nSCMGaG8cE/PV5s1PMzF/ZfuOKWwTKcTQCm07oJP0Dkl3SbpH0gntWk/zSkUH0AHTfbFg6vdhuh2J\n5q4Ijh8gfoFaB4jVOxrTHVA2s2NSKqWxPaQSR7/plryVwvaRQgxp5O8UYpjedHmzFSfq0tgm+o/z\n1kw4hnFFxzGXgw46aMqreNXz2nFVL43tsnltOaCTtBXwZeDtwD7AByW9qh3rat6GogNIRDPvw9RX\nBCer13SodgzVOxrTvd7kHZO1UyaEygPAT37yk0k0A9iwwdtlp3VT3mpm+2jVFe40tlHHUE/15zxZ\n65twprFN9Jd+yVut4xjGFR3HJmAZU13Fq57Xjg5v0tgum9euK3T7A/dGxAMR8SxwIXBkm9ZlyZnu\nit7Mrvi1en1Tn5le5h6y+ldf5K1WN5m2NE1/Iqz3ht/pU32Rt8wyE/cPZ9t8vHKZU089tWUn8qvX\n3clawXYd0O0KPFgx/VA+bwoXAmcAZ/Dss0+1KaxaRju4rpSNFh0A4zG0e0djqgPG0UlLd7oTmMHB\nIU499VQXDnfeDPJWlqueeaaTuWrc6Ojolvvt3j6rX7/cBKa8jRa7fY4WuO6y0aIDyG3V4ituM2tK\n77xVmBnnLfhO24OqpTJvFWe06ABIIwZII47RGS5fvX84u+bjE5c5pmUnNiefSOvcSdO2DFsg6Q+B\nt0fEx/PpPwb2j4jjKpbxKUGzxPVT99+N5K18vnOXWcKct5y3zLpNs3mrXQOLPwzsVjG9KJ+3RT8l\nXDPrCtPmLXDuMrOkOG+ZWduaXP4MeLmkxZK2BT4AXN6mdZmZtYLzlpl1G+ctM2vPFbqIeF7S/wCu\nIjtoPDMi7mzHuszMWsF5y8y6jfOWmUGbaujMzMzMzMys/do2sHglSYskXSvp55Juk3RcPn+BpKsk\n3S3pSkk7tTGGuZKul7Q6j2FZp2OoiGUrSTdLuryIGCSNSrolfy9uKCKGfJ07SbpE0p35tvHGDm8T\nr8zfg5vzv09KOq6Az+OTkm6XdKuk8yRtW0AMx+ffi0K+n91ABQzem0LurIil0LyVr7PQnJHH0PHv\nq6QzJY1JurViXt11SjpJ0r35+3RYG2P4XL6OkqRLJe3YzhjqxVHx2F9L2ixpYbvj6BZF5K18vTPa\nZtsUQ+H5U973rFx/4fueifyGtGXfsyMHdMBzwF9FxD7A7wGfUDbw5YnANRGxF3AtcFK7AoiITcBB\nEbEvsAQ4XNL+nYyhwvHAHRXTnY5hMzAcEftGxP4FxQDwReCKiNgb+F3grk7GERH35O/BfsDrgN8A\nl3UyBkm7AH8J7BcRryVrBv3BDsewD/CnwOvJvhvvlrRnJ2NInYobvLfw3Fmh6LwFBeeMAr+vZ5Nt\ne5VqrlPSq4GjgL2Bw4GvSC0Zx6BWDFcB+0TEEuDeDsRQLw4kLQIOBR6omLd3G+NIXoF5C2awzbZR\n4fnT+54TpLDvWehvCLRx3zMiOn4jGwTlELI3ciCfNwjc1aH1bw/cCLyh0zGQ9UB1NTAMXJ7P63QM\nvwReVDWv0zHsCNxXY35R28RhwH92OgZgF7IdkAVkO4eXd/q7AbwP+GrF9N8DnwLuLOKzSPEGvAn4\nQcX0icAJBcRRSO5MJG8VnjOK/L4Ci4Fbp/u/q7dN4AfAG9sRQ9Vj7wXObXcM9eIALgFeQ/b7trAT\ncaR+KzpvNbrNdjAe73v28b5nCr8hNdbdsn3PTl2h20LSENlZilV54GMAEbEW2LnN695K0mpgLXB1\nRPys0zEAXyDbWa4sXux0DAFcLelnkj5aUAy7A49LOju/7HyGpO0LiKPs/cD5+f2OxRARjwCfB9aQ\ndTX9ZERc08kYgNuBt+SX+7cH3gm8rMMxpG4Gg/e2R5G5kzTyVuE5I5Hva9nOddZZva0+TGe21Y8A\nVxQRg6QjgAcj4raqh4p6L1JReN6qUm+bbTvveyaRw4ve9yz8N6SGlu17dvSATtI84FvA8RGxkYkb\nFjWmWyoiNkd22XsRsH/e1KxjMUh6FzAWESVgqmYf7e6p5oDILvW+k6wJwltqrLPdMcwB9gP+JY/l\nN2RnDzsdB5K2AY4gO8Nba53t3CbmA0eSncncBdhB0oc6GUNE3AUsJzt7dwWwGni+1qLtisGmVmTu\nTChvFZ4zUvi+TqGw76ekvwOejYgLClj3C4CTgWWdXrc1rSPbrPc9k8nhRe97Fv4bUqnV+54dO6CT\nNIfsC3VuRHw3nz0maSB/fBB4rBOxRMRTwAjwjg7HcABwhKT7gQuAt0k6F1jbyfchIh7N//6KrAnC\n/nT+s3iI7Izqjfn0pWRftCK2icOBmyLi8Xy6kzEcAtwfEesi4nmydtT/rcMxEBFnR8TrI2IY2ADc\n3ekYEtfQ4L3tkEDuTCJvkUbOSOL7mqu3zofJrrCXtXVblbSUbAft6IrZnYxhT2AIuEXSL/N13Sxp\nZwr83iYitf+/49+TBPLnFt73LHzfM4XfkEot3ffs5BW6s4A7IuKLFfMuB5bm948Bvlv9pFaR9OJy\njzH5Gb1DyWqEOhZDRJwcEbtFxB5kg39eGxEfBr7XqRgkbZ+frULSDmTtd2+jg+8DQH5Z+UFJr8xn\nHQz8vNNx5D5IluTKOhnDGuBNkrbLi/UPJita7uj7IOkl+d/dgN8nawJQxGeRqiIH7y00d6aQt/I4\nUsgZRX5fxcSz6/XWeTnwAWW9b+4OvBy4oR0xSHoHWTOuIyLr/KEytnbFMCGOiLg9IgYjYo+I2J1s\np23fiHgsj+P9bYwjdUUPOt7oNttO3vdMIIensO+ZyG9Ipdbue86k4G62N7KzA88DJbLmXDeTnaFY\nCFxDdjXgKmB+G2N4Tb7eEnAr8Hf5/I7FUBXPgYwXpnbyfdi94nO4DTixqPeBrIehn+XxfBvYqdNx\nkBUp/wp4YcW8TsewjCzB3wqcA2xTQAz/QVZLt5qsF6rCvhup3vKcdTdZb34ndmidhefOqngKyVsV\n608hZ3T8+0p2guURYBPZQeWxZB2z1FwnWe9ov8jjPKyNMdxL1knMzfntK+2MoV4cVY/fT94pSjvj\n6JZbEXlriu2l7jbbphgKz59437O83iT2PUngNySPo+X7nh5Y3MzMzMzMrEt1vJdLMzMzMzMzaw0f\n0JmZmZmZmXUpH9CZmZmZmZl1KR/QmZmZmZmZdSkf0JmZmZmZmXUpH9CZmZmZmZl1KR/QmZmZmZmZ\ndSkf0JmZmZmZmXUpH9CZmZmZmZl1KR/QmZmZmZmZdSkf0JmZmZmZmXUpH9CZmZmZmZl1KR/QmZmZ\nmZmZdSkf0JmZmZmZmXUpH9D1OUm3S3prG1//l5Lelt8/SdIZ7VqXmfUeSWdL+of8/psl3Vl0TNXy\nGNdJWlV0LGbWWZIOlPRg0XFYf5tTdABWrIj4nQ6u67ROrcvMek9EXAfsXXQclSS9GTgY2CUi/qvo\neMysEFF0ANbffIXOzMx6nqSt2/TSQ8CoD+bMzKwoPqDrc+UmkZKWSbpI0jmSnpJ0m6T9KpY7QdJD\n+WN3Sjoon7+lOVQ+XbfpQb6Oc/P7iyVtlvQnkh6Q9Jikk9v9/5pZZ+S55URJP5f0hKQzJW2bP/Zu\nSaslrZd0naTXVDxvX0k3SXpS0oXAdhWPTcgvkvaTdHO+7MWSLqxonnmgpAcl/a2kR4GzGlj3SyV9\nK89H90n6y2n+x48AXwV+L8+NyyQdI+k/q5bbLGmP/P7Zkr4s6fv5c34qafeKZfeRdFX+nj0q6cTZ\nvP9m1lpT5bSq5U6Q9Iv8+327pPdWPLanpBFJG/I8c0HFY5sl/bmke/Kc9g+S9pD043z5CyXNyZed\nL+l7+Ws8kd/fpYH/YYGksyQ9nD/v2xWPfUzSvZIel/QdSS+tiu3P8tjWSfpyM++ltZ4P6KzSe4Dz\ngZ2A7wH/AiDplcAngNdFxI7A24HRKV5nqqYH1Y8dALwCOAT4X5L2mlXkZpaio4FDgT2BvYC/l7QE\nOBP4GLAQ+DfgcknbSNoGuAw4J3/sEuAPq14zAPJlv012oLYQuAD4/aplB4H5wG7AxyXtO8W6RZb3\nVgMvJWtGebykQ+v9cxFxFvDfgZ9GxI4RcWpljNUxV3g/sCyP7T7gM/n/NA+4Grgij+HlwL/XW7+Z\nddyknFZjmV8AB+T7S6cC35Q0kD/2j8CVETEfWAR8qeq5hwH7Am8C/pYsRx0NvAx4DfDBfLmtyHLf\ny8jy29NAIwdZ3wReQNZ0fWfgCwDK+jr4LPA+styzBriw6rnvAl4H/C5wlKTDGlifdYgP6KzSdRFx\nZUQEcC7w2nz+88C2wO9ImhMRayLily1YXwCnRMQzEXErcAtZojCz3vCliHgkIjaQHbQcDXwc+D8R\ncWNkzgU2ke3AvAmYExH/HBHPR8SlwM/qvPbvAVtHxJfzZS8Dbqha5nlgWUQ8GxGbyA7k6q37DcCL\nI+Iz+euNAl8DPtCC90FV05dFxE0RsRk4D1iSz38P8GhErMjz4m8iot7/b2adVyunTRARl0bEWH7/\nEuBeYP/84WeBxZJ2zb/jP6l6+vL8e38ncDtwVUQ8EBG/Bn5AdrBHRKyLiMsiYlNE/AY4DThwqsAl\nDZKdkP+ziHgqz3Pl1gRHA2dGxC0R8SxwElnLg90qXuK0iPh1RDwIrGQ8b1kCfEBnldZW3H8a2E7S\nVhFxH/A/gVOAMUnn54mhFcaq1jmvRa9rZsV7qOL+A8AuZGeT/yZvtrNO0nqyM9W75LeHq17jgTqv\n/dIay1Y39/5VvnNSthj46zrrXgzsWvXYSWRnsVutOteW894isit2Zpam6pz20uoFlJWSlJt1rwf2\nAV6cP/wpsn3vG5SVthxb9fTHKu7/lon7SL8lzxWSXiDp3ySNStoA/AiYn7c0qOdlwLqIeKrGY7tQ\nkWvzg8QngF0rlvH+WsJ8QGcNiYgLI+ItZDs9AMvzv78Btq9YdFJyM7O+9bKK+7uRHYA9CPxTRCzM\nbwsiYl5EXAQ8ysQdiPLzaqm17MuqpqubOj4IfKbOuh8E7q96bKeIeE+D/2vZhJw4w5NfD5I15TKz\nNFXmmMXAI5UP5le0zgD+Is8hC4Cfk1+lj4jHIuLjEbErWXPtr5Tra2fob8jKVd6QN98sDz811QHd\ng8BCSTvWeOwRxvfvkLQD8CImHsBawnxAZ1MRZDV0kg7Ki3+fITtLtDlfpgS8My+0HQSOn+nrm1nP\n+oSkXSUtBP6OrCbja8CfS9ofsh0HSe/MdyB+Cjwn6S8lzZH0B4w3Var2U+B5SZ+QtLWkI6dYtuyr\nwH+vs+4bgF8r60Rlu/w195H0+hn+z7cA+0h6raS5ZLVyjXZp/n1gUNJxkraVNK8cq5kloTKnncx4\nnVl5f2YHsv2jxyVtlV+B2zI8lKT3SSqfiNqQL1ven5qJeWT7Yk/lsZwy3RMiYi1Zs82v5J2qzJH0\nlvzhC4BjK/LWZ4FVefNK6wI+oLNGOjCZC5wO/IrsLM5LyJoiQVZrdytZJyk/ZHIR7Uw6SPE4Lma9\n5XzgKrJOAu4luzp2E/BR4MuS1gH3AMcA5M0j/wA4lqy5zx8Bl9Z64YplPwqsJ6sB+R5ZTVxN+bo/\nVmfdm4F3k9WF/JKs6dNXgVpns+uKiHuBfyDrzOQe4D+nfsaE524k63DhCLJmmfcAwzNZv5m11aSc\nls8PgLz27fPAKrLv8D7AdRXPfwNwvaSngO8Ax+X1ulteo8JU+0QryFoCPA78hKwjpUZ8GHgOuIus\nCeXxedz/DnyarKOph4HdmVg/7P21xCnr/6KBBaWtgBuBhyLiCEkLgIvILtGOAkdFxJP5sicBHyHb\naI6PiKvaELuZWV2SziTbQR+LiNfm85y3OkTSL4E/jYhrO7jOVcC/RsQ5nVqnWadIGgWeJLui82xE\n7D9VTrPWKiKnmTVqJlfojgfuqJg+EbgmIvYCriW/YiPp1cBRZF2iHk52addN68ys084m69GrkvNW\nD0+8MOUAACAASURBVJH0VkkDefPIY8i69f5h0XGZtclmYDgi9o2IclPcmjnNzPpLQwd0khYB7ySr\nfSg7kmysIPK/5YETjwAujIjn8svIld21mpl1RERcR9YUr5LzVud0oknOXmQ1a+uBTwJ/WO4uvJUk\nXSHp18oGCn6q4r4H/bZOEpP32+rlNGu9rmhmWJWrKvPVAUXHZu0zp8HlvkDW1epOFfMGKsbZWCup\n3LXzrmTF6mUPM7knMjOzIuzsvNUZETGbnttmuo6vktW5tXs972z3OswaEMDVkp4H/i0ivkb9fTFr\nsU7ktFaIiBcWHYN13rQHdJLeRVaDUpI0PMWiMzpzIakrznSY9bOI6PVmhzPOQ85dZmnr4bx1QEQ8\nKuklwFWS7qbBziqct8zS1mzeaqTJ5QHAEZLuJ+vW9G2SzgXWShqALePslAdDfJiJ43QsYvLgrwBE\nRNtvy5Yt83oSXU8v/S+9uJ4eNdZs3oLW5q6BgcUTXntgYHFXbzeOvfvj7ubYe1lEPJr//RVZD4n7\nUz+n1Xp+z9+6dbv1/9nf/2crTHtAFxEnR8RukV1q/gBwbUR8mKx76KX5YscA383vXw58IB9DZ3fg\n5WTj+xRidHTU60l0Pb30v/TienqEmDje4eUklrfGxh4gO6me3bLp1uvm7aZbY+/WuKG7Y+9FkraX\nNC+/vwNwGHAb9XNaX+qX7db/p1VrtIaultOBiyV9BHiArIc4IuIOSReT9Yj5LPAX0arDTzOzBkk6\nn2wMrxdJWkM2wPPpwCXOW2bWZQaAy/Kmk3OA8yLiKkk3UmNfzMz6y4wO6CLiR8CP8vvrgEPqLHca\ncFrT0bXA0qVLvZ5E19NL/0svrqfbRcTRdR5KPm+1QzdvN90ae7fGDd0dey+KiF+SDXpfPb/uvlg/\n6pft1v+nVWt4YPGWr1jyCXCzhEkierdzgVlrde7KhrurfD21rE29Wb9x3qrN+1xm6WpF3prJwOJd\naWRkxOtJdD299L/04nqst3TzdtOtsXdr3NDdsVv/6oftdnBwCEkTboODQ0WH1Rb98Hm2SjM1dE37\n1KdO2nL/hS/cgRNO+BRz584tMCIzMzMzszRlHWetJCsRL8/zRel+V2iTS/jslum5c7/ET37yffbb\nb79C4jGzidx0qTY3uTRLl/NWbW5y2Tsm/2aAfze6WyvyVqFX6GD8Ct3cuZcUGIeZmZmZmVn3cQ2d\n11PYenrpf+nF9Vhv6ebtpltj79a4obtjt/7VP9vtSNEBdET/fJ7N6/kDOjMzMzMzs15VcA3d+Lp3\n3HE/Vq78mmvozBLhWpTaXENnli7nrdpcQ9c7XEPXezoybIGkuZKul7Ra0m2SluXzl0l6SNLN+e0d\nFc85SdK9ku6UdFgzAZqZmZmZmVlt0x7QRcQm4KCI2BdYAhwuaf/84f8dEfvltx8CSNobOArYGzgc\n+Iqy0wmF6LW6pl5aTy/9L724Hust3bzddGvs3Ro3dHfs1r/6Z7sdmXaJ6vHqunGsuv75PJvXUA1d\nRDyd351L1jNm+bpurQO1I4ELI+K5iBgF7gX2r7GcmZmZmZm1WDZeXWy5ZdPWqxqqoZO0FXATsCfw\nLxFxUt70cinwJHAj8NcR8aSkLwE/jYjz8+d+DbgiIr5d9ZquoTNLmGtRanMNnVm6nLdqcw1d72i0\nhs6/Ld2jY+PQRcRmYF9JOwKXSXo18BXgHyIiJP0T8HngozNb/VJgCIBNm8a48cYbtxzQlS+zDg8P\ne9rTnu7AdKlUYsOGDQCMjo5iZmZm3WAuBVY3WQoiYkY34NPAX1XNWwzcmt8/ETih4rEfAm+s8ToB\nseW24477xk033RSttnLlypa/ptfTPevwemYvSw8zyw/9cMvfl5apzoWtfv2yTm037dCtsXdr3BHd\nG7vzVmfyVqq6dbudiew3Y+Wk342J0537bWmnfvg8I1qTtxrp5fLFknbK778AOBS4S9JgxWJ/ANye\n378c+ICkbSXtDrwcuGEWx5pmZmZmZmY2hWlr6CS9BjiHrAOVrYCLIuIzkr5B1uvlZmAU+LOIGMuf\ncxLwp8CzwPERcVWN13UNnVnCXItSm2vozNLlvFWba+h6R70auunn+bclVR2poYv4f+zdf7xdVX3n\n/9ebRCI/hMQquZVoLooIWG3Qis6g5ZZfpdoBRzuUYq3Bqp1Rv/78tgTbmdT+CmFGTdXhMaMgDQhF\ntDiEFiVQcnTECiZwJJJIsXoBo7kqEDRjJxLymT/2vsm55+5z77n3/Nprn/fz8TiPnL3P3nt99jk7\n6561z/qsFVuBaa2siPi9GfZZA6zpJDAzMzMzMzObWVvTFqSsanODVamcKp1LFcuxakn5ukk19lTj\nhrRjt+E1PNdtbdAB9MXwfJ6dq3yDzsyskaT3SvqmpHslXZPn+y6RtFHS/ZJumcwbNjMrE0kHSbpb\n0oZ82XWXmbU3D11PCnYOnVmpVTEXRdKzgK8Ax0fEzyV9BrgZOBF4JCIulXQRsCQiVrU4hnPozEqq\nivVWI0nvBV4KHBER50haSxt1l3PoqsM5dNXTjXrLv9CZ2bBZABwmaSFwCLADOJds8Cfyf187oNjM\nzApJWga8Gri8YbXrLjOrfoOuanlNVSqnSudSxXKqKCK+D3wIeIisIfd4RNwGLJ0cpTcidgJHDS7K\n3kj5ukk19lTjhrRjr7CPAH/I1J9dKl93zcXwXLe1QQfQF8PzeXau8g06M7NJkhaT3dFeDjyL7Je6\nNzC9r4r7pZhZaUh6DTAREXWyvnStuO4yG0KzTluQurGxMZdT0nKqdC5VLKeizgC+ExGPAkj6PPBv\ngQlJSyNiQtII8MOZDrJy5UpGR0cBWLx4MStWrNj/uUzeUWx3OVMDDizXarV5H6+qy5PKEk87y2Nj\nY6WKp4rL69ato16v7///WGGnAOdIejVZV/GnSboa2Nlu3dXNesvLg13O1Gj8uzF1ucZUU5cHHf+w\nL/ei3vKgKGZWqIqDC0g6GbgCeBmwB7gS+DrwHODRiFjrQVHM0lXFequZpFOB9+eDolxKNijKjHWX\nB0WpDg+KUj19GRRF0iJJd0q6R9JWSavz9S2HypV0saQHJG2XdFYnAXZqslXscspXTpXOpYrlVFFE\n3AV8DrgH+AbZX7xPAGuBMyXdD5wOXDKwIHsk5esm1dhTjRvSjn3IXELF6665GJ7rtjboAPpieD7P\nzs3a5TIi9kj6tYj4maQFwB2SvgC8HritYajci4FVkk4EzgNOAJYBt0l6vm8NmVkZRMQHgQ82rX6U\nrDummVmpRcSXgC/lz113WZsW5b/uHbB06XJ27hyfca+RkVEmJh6c837WX3PqcinpUODLwH8CrgZO\nbei3XYuI4yWtAiIi1ub7fAH404i4s+lY7nJpVmLD0HVpPtzl0qy8XG8Vc5fL6uiky2XRNrNdF63K\n8/XUPX2bh07SQZLuAXYCt0bE12k9VO7RwMMNu+/I15mZmZmZWUmNjIwiaf/D0tBWgy4i9kXESWRd\nKE+W9EISGea7anlNVSqnSudSxXKsWlK+blKNPdW4Ie3YbXgNz3Vb69mRs+6V0fAYnOH5PDs3p2kL\nIuInkmrA2bQe5nsH8OyG3Zbl6wqsBEYB2LNngs2bN+/vctmtIUIn9XoI0nq93tPjV/F86vV6aYaQ\n9flk8e/atQuA8fFxzMzMzKz8Zs2hk/QM4ImIeFzSIcAtZKMonUrBMN/5oCjXAC8n62p5KzBtUBTn\n0JmVm3NRijmHzqy8XG8Vcw5duooGJellDl3R36Tp+z2VbOafAzxQyvx1o95q5xe6XwTWSzqIrIvm\nZyLiZklfA66X9GbgQbKRLYmIbZKuB7YBTwBvdy1iZmZmZjY3B7pATirD/Yo9NDfyJibKENfwmjWH\nLiK2RsRLImJFRLw4Iv4yX/9oRJwRES+IiLMiYlfDPmsi4tiIOCEiNvbyBGZTtbymKpVTpXOpYjlW\nLSlfN6nGnmrckHbsNryG57qtDTqAvhiez7NzbQ2KYmZmZmZmvdM8wqRHmbR2zWkeuq4W7Bw6s1Jz\nLkox59CZlZfrrWLOoUtDe3PM9XYeuvZy6Dw3XTf1bR46MzMzMzMzK5/KN+iqltdUpXKqdC5VLMeq\nJeXrJtXYU40b0o7dhldq1+38J/GudSmCRaXu4pna5zlIc5qHzszMzMzMOjf4ESynj1ZZjlE0ba6c\nQ2dmhZyLUsw5dGbl5XqrmHPoyml++WrdzaHr5n6+xubHOXRmZmZmZmZDrPINuqrlNVWpnCqdSxXL\nsUGZntMwMjLa8VFTvm5SjT3VuCHt2G14Dc91Wxt0AH0xPJ9n5yrfoDMzG5SiOYUWLDhslgT0yZyG\nA48sz8LMzMxsullz6CQtA64ClgL7gE9ExMckrQbeCvww3/QDEfHFfJ+LgTcDe4F3R8TGguM6h86s\nxJyLUmwuuSjzm1PIuQlm8+V6q5hz6MrJOXQG3am32hnlci/wvoioSzoc2CLp1vy1D0fEh5uCOgE4\nDzgBWAbcJun5rknMzMzMzMy6a9YulxGxMyLq+fPdwHbg6PzlotbkucB1EbE3IsaBB4CTuxPu3FUt\nr6lK5VTpXKpYjlVLytdNqrGnGjekHbsNr+G5bmuDDqAvhufz7NyccugkjQIrgDvzVe+UVJd0uaQj\n83VHAw837LaDAw1AM7OBk3SkpM9K2i7pPkkvl7RE0kZJ90u6paFOMzMbKEmLJN0p6R5JW/O0F1xv\nmRnMYR66vLtlDfjziLhR0jOBH0dESPoLYCQi3iLpY8A/RcS1+X6XAzdHxA1Nxwt4EzAKwKJFn+Sj\nH13N2972NuBAq3xsbMzLXvZyH5br9Tq7du0CYHx8nPXr11c2F0XS3wBfiogrJS0EDgM+ADwSEZdK\nughYEhGrCvZ1Dp1ZSVU5h07SoRHxM0kLgDuAdwGvp8v1lvWPc+gMulNvtdWgy7/w/D3whYj464LX\nlwM3RcSLJa0CIiLW5q99EVgdEXc27eNBUcxKrKpfjCQdAdwTEc9rWv8t4NSImJA0AtQi4viC/d2g\nMyupqtZbjSQdCnwZ+E/A1XS53rL+cYPOoL8Ti38K2NbYmMsrjkmvA76ZP98AnC/pYEnHAMcCd3US\nZCeqltdUpXKqdC5VLKfCjgF+LOlKSXdL+kT+BWlpRExAljsMHDXQKLss5esm1dhTjRvSjr2qJB0k\n6R5gJ3BrRHyditdbczU8121t0AH0xfB8np2bdZRLSacAbwC25hVJkHVNukDSCrKpDMaBPwCIiG2S\nrge2AU8Ab/dtITMrkYXAS4B3RMRmSR8BVjH9dqPrLTMrjYjYB5yU9zL4vKQX4nrLzGijQRcRdwAL\nCl764gz7rAHWdBBX10zmCLmc8pVTpXOpYjkV9j3g4YjYnC//HVmDbkLS0oauSz9sdYCVK1cyOjoK\nwOLFi1mxYkXL/MQDd1LHOKA2h+UajcqQbzmI5Ulliaed5bGxsVLFU8XldevWUa/X9/9/HAYR8RNJ\nNeBselRvebk/ywe0Wh5rsTy5rtVyu8dvt7zZyp96vLK8v2Vd7kW91fagKN3mHDqzcqtyLoqkLwFv\njYh/zkeLOzR/6dGIWOtBUczSVNV6S9IzgCci4nFJhwC3AJcAp9Llesv6xzl0Bv3NoUvW9LsgLqcs\n5VTpXKpYTsW9C7hGUh34ZeCvgLXAmZLuB04n+7JUGSlfN6nGnmrckHbsFfWLwKa8zroTuCUibqbi\n9dZcDc91Wxt0AH0xPJ9n52btcmlmVjUR8Q3gZQUvndHvWMzMZhMRW8lyf5vXP4rrLSuhkZFRJiYe\nnLJu6dLl7Nw5PpiAKs5dLs2sUFW7LnXKXS7Nysv1VjF3uSynKne5bPX3z9fhdO5yaWZmZmZmyRkZ\nGUXSlMfIyOigw0pS5Rt0VctrqlI5VTqXKpZj1ZLydZNq7KnGDWnHbsNreK7b2qAD6IqsS2ZMeTR2\n0xyez7NzlW/QmZmZmZmZVZVz6MyskHNRijmHzqy8XG8Vcw7d4BUNEpIZ3hw659ll+pJDJ2mZpNsl\n3Sdpq6R35euXSNoo6X5Jt0g6smGfiyU9IGm7pLM6CdDMzMzMLGVF3QvNuqWdLpd7gfdFxAuBfwO8\nQ9LxwCrgtoh4AXA7cDGApBOB84ATgN8ALlPWBB+IquU1VamcKp1LFcuxMlnUcdJ4ytdNqrGnGjek\nHbsNr+G5bmuDDqAvhufz7NysDbqI2BkR9fz5bmA7sAw4F1ifb7YeeG3+/BzguojYGxHjwAPAyV2O\n28xsiOyhVdK4mZmZDbc55dBJGiW7LfBLwMMRsaThtUcj4umSPgb8U0Rcm6+/HLg5Im5oOpZz6MxK\nzLkoxQaVQ9e8zbDlGJi1w/VWMefQDd78/h7Md5vB7Occuvnp6zx0kg4HPge8O/+lrvndHq5338zM\nzMzMbMAWtrORpIVkjbmrI+LGfPWEpKURMSFpBPhhvn4H8OyG3Zfl6wqsBEYB2LNngs2bN+//hW6y\n3+zY2FhHy5PrunW8Vsvr1q1jxYoVPTt+Fc+nXq/znve8p2fHn1xufu98Pq3j37VrFwDj4+NYtdRq\ntf2fd2pSjT3VuCHt2G14Dc91WwPGBhxD7w3P59kFETHrA7gK+HDTurXARfnzi4BL8ucnAvcABwPH\nAN8m79rZtH9A7H8cccRJsWXLlui2TZs2df2YLiedMlzO/GXVw+z1w7A98vel7fewsZ7LHs3rZlsu\n3mau+nXd9EKqsacad0S6sbve6rzeSlmZr9v5/T1otc2mee432zad7dfO+c5lmzJ/nt3UjXpr1hw6\nSacAXwa2ciAr/wPAXcD1ZL/GPQicFxG78n0uBn4feIKsi+bGguOGc+jMysu5KMWcQ2dWXq63ijmH\nbvCcQze/bYZBN+qtWbtcRsQdwIIWL5/RYp81wJoO4jIzMzMzM7NZtD0oSqr6NYeFyylnGS7HLJPy\ndZNq7KnGDWnHbsNreK7b2qAD6Ivh+Tw7V/kGnZmZmZmZWVXNaR66rhbsHDqzUnMuSjHn0JmVl+ut\nYs6hGzzn0M1vm2HQ13nozMysLBYhaf9jZGR00AGZmZnZgFS+QVe1vKYqlVOlc6liOVZmezgw6HAw\nMfHgrHukfN2kGnuqcUPasdvwGp7rtjboAPpieD7PzlW+QWdm1kzSQZLulrQhX14iaaOk+yXdIunI\nQcc4N1N/sfOvdmbVImmZpNsl3Sdpq6R35esTr7vMrBucQ2dmhaqciyLpvcBLgSMi4hxJa4FHIuJS\nSRcBSyJiVYt9S5FD184+w5aHYFbVekvSCDASEXVJhwNbgHOBC2mj7nIO3eA5h25+2wwD59CZmc2R\npGXAq4HLG1afC6zPn68HXtvvuMzMWomInRFRz5/vBrYDy3DdVUojI6PTek0YNPcmse6pfIOuanlN\nVSqnSudSxXIq7CPAHzL1tuDSiJiA7IsTcNQgAuullK+bVGNPNW5IO/aqkzQKrAC+xhDUXXNRlus2\ny2uOpkc31bp8vH6Zmv89m7J8nimYtUEn6QpJE5LubVi3WtL38hyUuyWd3fDaxZIekLRd0lm9CtzM\nbK4kvQaYyO90z3R7cLj6e5hZEvLulp8D3p3/UtdcV7nuMhtCC9vY5krgY8BVTes/HBEfblwh6QTg\nPOAEsq4At0l6/iA7bo+NjbmckpZTpXOpYjkVdQpwjqRXA4cAT5N0NbBT0tKImMhzVX4400FWrlzJ\n6OgoAIsXL2bFihX7P5fJO4oHPqda/u8YB9TmsFxjqrktN8eT6nKK5zM2NlaqeKq4vG7dOur1+v7/\nj1UmaSFZY+7qiLgxXz3Rbt01l3rLy50tZ2p0Xo8379/q+L0ub7bya8BTZuhGObfyB/35pVhvtTUo\niqTlwE0R8eJ8eTWwOyI+1LTdKiAiYm2+/AXgTyPizoJjelAUsxKr6uACkySdCrw/HxTlUrKBBdZ6\nUBSzdFW53pJ0FfDjiHhfw7q1wKOz1V0eFKW/2qv7i9aVY3CT/u3nv10w+EFR3impLunyhmFyjwYe\nbthmR75uYCZbxS6nfOVU6VyqWM6QuQQ4U9L9wOn5cqWkfN2kGnuqcUPasVeRpFOANwCnSbqnId1l\nLRWvu+ZieK7b2qAD6Ivh+Tw7106XyyKXAX8WESHpL4APAW+Z+2FWAqMA7NkzwebNm/f/Qtetnzcn\n9frn03q93tPjV/F86vV6aX7+9vlk8e/atQuA8fFxqi4ivgR8KX/+KHDGYCMyMysWEXcAC1q87LrL\nbMjNq8tlq9cKulx+EVjtLpdm6aly16VOuMulWXm53irmLpf95S6XnW0zbNdqP7tcioYR4fLE20mv\nA76ZP98AnC/pYEnHAMcCd3USoJmZmZmZmRWbtUEn6Vrgq8Bxkh6SdCFwqaR7JdWBU4H3AkTENuB6\nYBtwM/D2Qd8SqlpeU5XKqdK5VLEcq5aUr5tUY081bkg7dhtew3Pd1gYdQF8Mz+fZuVlz6CLigoLV\nV86w/RpgTSdBmZlZb42MjOaT3x6wdOlydu4cH0xAZmZmNi9t5dD1pGDn0JmVmnNRilUlh65VbMOW\nu2DV4nqrmHPo+ss5dJ1t03ytNt+ArNrNx0FPW2BmZokYGRlF0v6HmZlZCrLGXOx/NPcusSFo0FUt\nr6lK5VTpXKpYjlVL8x/ElKR6zacaN6Qduw2v4blua4MOoC+G5/PsXOUbdGZmw2lR13+Ra/6VTxIj\nI6NdObaZmZnNj3PozKyQc1GKpZRD57w7Gzaut4o5h66/nEPX2TbN1+r097Naf3ecQ2dmZmZmZjbE\nKt+gq1peU5XKqdK5VLEcs7JI9ZpPNW5IO3YbXsNz3dYGHUBftP48F7n7f5NZ56EzMzMzMzMrhz00\nd9WcmBjuntaz5tBJugL4TWAiIl6cr1sCfAZYDowD50XE4/lrFwNvBvYC746IjS2O6xw6sxJzLkox\n59A5h87Ky/VWMefQ9Zdz6DrZ5qlkDbZmnf396kTzPHjQ3bnw+pVDdyXw603rVgG3RcQLgNuBi/OA\nTgTOA04AfgO4TJ7wyMzMzMzMZjX561t5ptmZPu1P+ebCm7VBFxFfAR5rWn0usD5/vh54bf78HOC6\niNgbEePAA8DJ3Ql1fqqW11Slcqp0LlUsx6wsk5Gnes2nGjekHbsNr+G5bmuDDqBPaoMOIBnzHRTl\nqIiYAIiIncBR+fqjgYcbttuRrzMzs8SkPBm5mZkNk+kDpSxYcNjQDJzS1jx0kpYDNzXk0D0aEU9v\neP2RiPgFSR8D/ikirs3XXw7cHBE3FBzTOXRmJeZclGLDlENXNPePc+iszFxvFXMOXX85h64Mxy7e\nbz7/D3qdP96Nemu+o1xOSFoaEROSRoAf5ut3AM9u2G5Zvq6FlcAoAHv2TLB58+b9DbrJn83Hxsa8\n7GUv92G5Xq+za9cuAMbHx7FhtGigXSvNzMxsHiJi1gdZq2trw/Ja4KL8+UXAJfnzE4F7gIOBY4Bv\nk/8KWHDMgNj/OOKIk2LLli3RbZs2ber6MV1OOmW4nPnLqofZ64dhe+TvS9vvYWM9lz2a1822XP59\neq1f13y3pRp3RLqxu97qvN5KWVmu2/bq107q5U09Ona/95ttm00dHLt4v25+nt28XqLD/+Oz5tBJ\nuhb4KnCcpIckXQhcApwp6X7g9HyZiNgGXA9sA24G3p4HamZWCpKWSbpd0n2Stkp6V75+iaSNku6X\ndIukIwcdq5kZZFNISZqQdG/DOtdZZga0mUPXk4KdQ2dWalXNRcm7iY9ERF3S4cAWspF7LwQeiYhL\nJV0ELImIVQX7t32fKsUcuvns4/t2VhYVrrdeCewGrooD4xmspY06K9/W99f7yDl0ZTh28X7z+X+Q\nQg7dfEe5NDNLUkTsjIh6/nw3sJ0s37fVdCxmZgMVc5tCyvqsLFO82Nw1f3apjoRZ+QZd1eYGq1I5\nVTqXKpYzDCSNAiuArwFLo3g6FhuwVK/5VOOGtGMfIq2mkBpag7pu+z/FS60PZZRBreclNH92ZZsw\nvF2Vb9CZmRXJu1t+Dnh3/ktd81/hOf1Vfv/7/4SnPvVpUx5mZn3kPpVmQ2q+0xYkY3JYdpdTvnKq\ndC5VLKfKJC0ka8xdHRE35qtbTccyzcqVKxkdHQVg8eLFrFixgq1bv8WePZcBv5BvtQt4AwfuMI41\nHKE2h+UaU/Vqubm8dpezu+K9nmKjsaxeHL8Xy2NjY6WKp4rL69ato16v7///OGTarrOguN4qy+eY\n0vLIyGiLX3FqdL8eb96/1fF7Xd5s5c+3vHbLby5vvuW3mppnannNn/9s5ZWh3vKgKGZWqKqDCwBI\nugr4cUS8r2HdWuDRiFg7n0FRzjrrt7j11vOB38rXfB54HakNcOJBUSxlFa+3RoGbIuJF+XJbdVa+\nrQdF6ZL5DXbV7rpBH7vf+5Uzpub/Kx4UpQT61Z/a5ZSzDJdjzSSdQvbT2WmS7pF0t6SzyebXnDYd\niw1eqtd8qnFD2rFX0VymkBpmw3Pd1gYdQJ/UBlDmoimDpHR7kJteDcJS+S6XZmaNIuIOYEGLl8/o\nZyxmZu2IiAtavOQ6y6yr9lD8K153HBiEZXK5O8d2l0szK1TlrkudcJfL5n2eSvYHMLN06XJ27hzH\nbBBcbxVzl8vucZfLbu6XdkzdmdMuq67c5dLMzAZo8m5m2kM+m5mZtW9q18wFCw6b1lWzn3PaddSg\nkzQu6Rt5Hspd+bolkjZKul/SLZKO7E6o81O1vKYqlVOlc6liOWZlkeo1n2rckHbsNryG57qtDTqA\nPqkNOoAZTL2ZuW/fz6Ys9/sGZ6e/0O0DxiLipIg4OV+3CrgtIl4A3A5c3GEZZmZmZmZmVqCjHDpJ\n3wV+JSIeaVj3LeDUhnlRahFxfMG+zqEzKzHnohRzDt1s+0zNqQM46KBD87uXGefZWa+43irmHLr5\naT3nXBo5XuXfr+oxTf97mClfDl0At0r6uqS35OuWRsREHtxO4KgOyzAzs2RM7YZS1BWl+QtS0Zz6\noQAAIABJREFU8zDO/c49MDMrcmBEwsaHWbum/z3slU6nLTglIn4g6ZnAxnwulOZoZ4h+JTAKwJ49\nE2zevHn/L3RznXW91fLkum4db6ZZ31esWNGz41fxfOr1Ou95z3t6dvzJ5eb3zufTOv5du3YBMD4+\njlm/NA/jnK07cLOyVqvtv1ZTkmrckHbsNryG57qtAWMDjqEfagzHeXZBRHTlAawG3g9sJ/uVDmAE\n2N5i+4DY/zjiiJNiy5Yt0W2bNm3q+jFdTjpluJz5y6qH7tQPVXrk78s0Z575+oDPNtRrN0RzPZc9\nmtfNtpz6PkXrFjXfsizcZ1K/rvluSzXuiHRjd701t3qrarp93bZXn823Huykjt1Uspg6OZeZttnU\nwbF7FVMv9iNihv+/7TzmnUMn6VDgoIjYLekwYCPwQeB04NGIWCvpImBJRKwq2D9wDp1ZaTkXpZhz\n6Oa6z/yPO9+/Tza8XG8Vcw7d/LQ351zRulRyvAa9n2PqVg5dJ10ulwKfzxpmLASuiYiNkjYD10t6\nM/AgcF4nAZqZmZlZZ7LGSeaYY17Id77zzQFGY2bdNO9BUSLiuxGxIrIpC14UEZfk6x+NiDMi4gUR\ncVZE7OpWsO0kzs9nm24k31dtDrJ+lFOlc6liOWZlkeo1n2rckHbs1spk766fsmPH+IBj6Y3huW5r\ngw6gT2qDDiAZnQ6K0lfFifNPnXLXKdO4TY2JiV+b8TiNyfdmZmZmZmap6Ggeuo4KnkcOXXt9mdud\nB2LqPu5bbjaVc1GKOYfOOXRWXq63ik39zrWbgw8eYc+e3QONKQXOoev1fo6pLPPQ9VRz18j5a54H\nwszMzMzMLH2lbtBNn9BxPmpdi2fGUiqWP+UcOpdjVhapXvOpxg1px27Da3iu29qgA+iT2qADSEap\nG3SD1Pzr4IIFh3V9IBUzM+se19tm1kovBsQzK4tS59BN77vcq/6xRTl2zLKP8zus2pyLUsw5dP3K\noZtaLy9dupydO8f3L4+MjOa9OJrNrd5uPk5zOZYW11vFnENX/J1ytu9xzqHr9X6OqVs5dEmNctk7\nkzl2jfz3wMxscKbWy82jEReNejyfetujHpuZWeqGoMtlrUfHXTTrfHe9UKU8rSqdSxXLMSuPGs11\n7vxMr7fno92uWyn/X005dhtec7tup9cHzd20y6s26AD6pDboAJLRswa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WQhHxL8B7gD8F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\nZpao/wdt6rbogm6Z0QAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xa3d67f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "all_data.hist(figsize=(15,9),bins=50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    500\n",
       "1    268\n",
       "Name: target, dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_data.target.value_counts()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}
